1. Field of the Invention
The present invention relates to the field of image processing, and particularly relates to an object tracking method and device on the basis of computer vision.
2. Description of the Related Art
At present, in the field of computer vision, object tracking is playing an important role. For example, the object tracking such as hand gesture tracking, pedestrian tracking, or vehicle tracking has been involved in a human-machine interactive system, a user monitoring system, a drive assistance system, etc.
In a case where the hand gesture tracking is taken as an example, a hand gesture control system is very convenient to users. An effective hand gesture recognition system may provide a smooth and available interactive way, and in this kind of hand gesture recognition system, hand tracking is a very important process.
In general, for the sake of user's operations, a hand tracking system should not require a user to wear any special device such as a special glove or a colorful dress. In addition, a hand is a kind of non-rigid object, and has characteristics such as fast motion, easy deformation, and self-shielding. As a result, a hand tracking technique is very challenging.
Currently the hand tracking technique mainly adopts color features to conduct tracking. As is known to all, color information is very sensitive to lighting conditions and background colors. As a result, the robustness of a tracking system adopting color features to conduct tracking is not good. In addition, a tracking system is also easily influenced by partial shielding or the change of the form of a tracking object, so that the tracking performance may be decreased. In order to increase the robustness of a tracking system, up to now, many studies have been conducted.
For example, in U.S. Patent Application Publication No. 2011/0211754 A1, an object tracking method for image processing is disclosed. The method includes receiving a depth image of a scene containing a human subject and receiving a color image of the scene containing the human subject. A part of a body of the subject is identified in at least one of the images. A quality of both the depth image and the color image is evaluated, and responsively to the quality, one of the images is selected to be dominant in processing of the part of the body in the images. The identified part is localized in the dominant one of the images, while using supporting data from the other one of the images. However, in this technique, the tracking result is still influenced by lighting conditions. As a result, in a case where there is a bad lighting condition, the tracking result mainly relies on tracking conducted on the basis of the depth image. In addition, since the tracking result obtained on the basis of the depth image mainly depends on a determination according to a predetermined threshold, the robustness may not be good.
Furthermore, in a paper entitled “Object Tracking Algorithm Based on CamShift with Dual ROI and Velocity Information Fusion” whose authors are QIAO Bing, L I Zhicheng, and H U Peng and which is published on “Information and Control”, in order to deal with the tracking divergence and the recapturing failure after occlusion of the continuously adaptive mean Shift algorithm (CamShift) to track objects passing the background with similar colors to them, an improved CamShift algorithm with dual region of interest (ROI) and velocity information fusion is proposed to track moving objects. The main idea of this algorithm is to divide the single ROI, which is used to specify the region to be tracked in CamShift, into two sub ROIs, of which one is the primary tracking region and the other one is the auxiliary tacking region. For each of these two sub ROIs, a CamShift tracker is designed respectively. Through the coordination of these two CamShift trackers in the process of tracking, the tracking robustness of this algorithm is enhanced and the interference problem due to similar color in the CamShift is solved. In this technique, however, in a case where the color of a tracking object is very similar to the background color, the tracking result may not be correct.